Schizophrenia (SZ) is a severe psychiatric disorder that affects 1% of the population worldwide and has a strong genetic influence on susceptibility. Recent genetic investigations of SZ, such as genome-wide association studies (GWAS) and structural genomic studies have made remarkable progress but leave a substantial part of the genetic risk unexplained, suggesting alternative models should be explored. We have designed a targeted sequencing experiment, by selecting coding and regulatory sequence in genomic intervals with high prior evidence for involvement in SZ. Our targets come from (i) genes that reside within SZ- associated copy number variant (CNV) intervals, or (ii) genes that show extreme transcriptional departures in SZ, for a total of ~600kb of sequence. We propose sequencing sample from the Molecular Genetics of SZ (MGS) collection and extracting sequence from the Genomic Psychiatry Cohort (GPC), resulting in a large, combined European ancestry (EA) discovery sample of 3,181 SZ cases and 3,500 matched controls. By limiting our target to a region that is small but likely enriched for SZ associated low frequency variants, we both lower the statistical threshold required for significance, and economically allow for a large sample size, giving us maximal power to identify new associations for SZ. We will then examine top hits for replication in the remaining GPC EA sample of 4,100 SZ cases and screened 5,400 controls. In addition, we propose adding another dimension of information, by functional evaluation of our most promising candidates. To accomplish this goal, in subsequent initial, exploratory work, we will generate and phenotypically characterize induced pluripotent stem cell (iPSC)-differentiated neurons from patients harboring associated mutations and from controls. If successful, our study will identify genes, putative mutations, and a mechanism of action by which those mutations contribute to SZ pathology. In this way we expect to refine our understanding of SZ and advance new, focused hypotheses to be tested. All data and biological materials will be rapidly shared through the designated NIMH repository (www.nimhgenetics.org) and dbGaP (dbgap.ncbi.nlm.nih.gov).